Overview

Dataset statistics

Number of variables8
Number of observations3613
Missing cells24
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory240.1 KiB
Average record size in memory68.0 B

Variable types

Numeric4
DateTime1
Categorical1
Text2

Dataset

Description행정동(읍면동) 성별 주민등록 평균연령에 대한 데이터입니다.행정동은 주민들이 거주하는 지역을 행정능률과 주민편의를 위하여 구분한 행정 구역 단위를 말합니다.
Author행정안전부
URLhttps://www.data.go.kr/data/15099157/fileData.do

Alerts

기준연월 has constant value ""Constant
행정기관코드 is highly overall correlated with 시도명High correlation
전체 평균연령 is highly overall correlated with 남자 평균연령 and 1 other fieldsHigh correlation
남자 평균연령 is highly overall correlated with 전체 평균연령 and 1 other fieldsHigh correlation
여자 평균연령 is highly overall correlated with 전체 평균연령 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 행정기관코드High correlation
행정기관코드 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:29:02.931141
Analysis finished2024-04-06 08:29:08.425030
Duration5.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3613
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8327467 × 109
Minimum1.1110515 × 109
Maximum5.280042 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:29:08.597339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110515 × 109
5-th percentile1.1380557 × 109
Q12.8260544 × 109
median4.311135 × 109
Q34.773033 × 109
95-th percentile5.2134532 × 109
Maximum5.280042 × 109
Range4.1689905 × 109
Interquartile range (IQR)1.9469786 × 109

Descriptive statistics

Standard deviation1.2616173 × 109
Coefficient of variation (CV)0.32916794
Kurtosis-0.20220735
Mean3.8327467 × 109
Median Absolute Deviation (MAD)5.21921 × 108
Skewness-0.98268915
Sum1.3847714 × 1013
Variance1.5916783 × 1018
MonotonicityStrictly increasing
2024-04-06T17:29:08.876380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111051500 1
 
< 0.1%
4681040000 1
 
< 0.1%
4684025800 1
 
< 0.1%
4684032000 1
 
< 0.1%
4684033000 1
 
< 0.1%
4684034000 1
 
< 0.1%
4684035000 1
 
< 0.1%
4684036000 1
 
< 0.1%
4684037000 1
 
< 0.1%
4686025000 1
 
< 0.1%
Other values (3603) 3603
99.7%
ValueCountFrequency (%)
1111051500 1
< 0.1%
1111053000 1
< 0.1%
1111054000 1
< 0.1%
1111055000 1
< 0.1%
1111056000 1
< 0.1%
1111057000 1
< 0.1%
1111058000 1
< 0.1%
1111060000 1
< 0.1%
1111061500 1
< 0.1%
1111063000 1
< 0.1%
ValueCountFrequency (%)
5280042000 1
< 0.1%
5280041000 1
< 0.1%
5280040000 1
< 0.1%
5280039000 1
< 0.1%
5280038000 1
< 0.1%
5280037000 1
< 0.1%
5280036000 1
< 0.1%
5280035000 1
< 0.1%
5280034000 1
< 0.1%
5280033000 1
< 0.1%

기준연월
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
Minimum2024-03-31 00:00:00
Maximum2024-03-31 00:00:00
2024-04-06T17:29:09.134200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:09.394346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
경기도
602 
서울특별시
426 
경상북도
335 
전라남도
323 
경상남도
310 
Other values (12)
1617 

Length

Max length7
Median length5
Mean length4.5773595
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 602
16.7%
서울특별시 426
11.8%
경상북도 335
9.3%
전라남도 323
8.9%
경상남도 310
8.6%
전북특별자치도 243
6.7%
충청남도 210
 
5.8%
부산광역시 205
 
5.7%
강원특별자치도 194
 
5.4%
인천광역시 160
 
4.4%
Other values (7) 605
16.7%

Length

2024-04-06T17:29:09.711162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 602
16.7%
서울특별시 426
11.8%
경상북도 335
9.3%
전라남도 323
8.9%
경상남도 310
8.6%
전북특별자치도 243
6.7%
충청남도 210
 
5.8%
부산광역시 205
 
5.7%
강원특별자치도 194
 
5.4%
인천광역시 160
 
4.4%
Other values (7) 605
16.7%

시군구명
Text

Distinct229
Distinct (%)6.4%
Missing24
Missing (%)0.7%
Memory size28.4 KiB
2024-04-06T17:29:10.506366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4664252
Min length2

Characters and Unicode

Total characters12441
Distinct characters143
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구
ValueCountFrequency (%)
서구 95
 
2.3%
북구 87
 
2.1%
동구 83
 
2.0%
중구 77
 
1.9%
남구 75
 
1.9%
창원시 55
 
1.4%
성남시 50
 
1.2%
수원시 44
 
1.1%
고양시 44
 
1.1%
청주시 43
 
1.1%
Other values (229) 3399
83.9%
2024-04-06T17:29:11.395173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 1641
 
13.2%
시 1632
 
13.1%
군 908
 
7.3%
463
 
3.7%
주 409
 
3.3%
천 343
 
2.8%
산 342
 
2.7%
성 297
 
2.4%
동 293
 
2.4%
양 274
 
2.2%
Other values (133) 5839
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11978
96.3%
Space Separator 463
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1641
 
13.7%
시 1632
 
13.6%
군 908
 
7.6%
주 409
 
3.4%
천 343
 
2.9%
산 342
 
2.9%
성 297
 
2.5%
동 293
 
2.4%
양 274
 
2.3%
남 263
 
2.2%
Other values (132) 5576
46.6%
Space Separator
ValueCountFrequency (%)
463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11978
96.3%
Common 463
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1641
 
13.7%
시 1632
 
13.6%
군 908
 
7.6%
주 409
 
3.4%
천 343
 
2.9%
산 342
 
2.9%
성 297
 
2.5%
동 293
 
2.4%
양 274
 
2.3%
남 263
 
2.2%
Other values (132) 5576
46.6%
Common
ValueCountFrequency (%)
463
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11978
96.3%
ASCII 463
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 1641
 
13.7%
시 1632
 
13.6%
군 908
 
7.6%
주 409
 
3.4%
천 343
 
2.9%
산 342
 
2.9%
성 297
 
2.5%
동 293
 
2.4%
양 274
 
2.3%
남 263
 
2.2%
Other values (132) 5576
46.6%
ASCII
ValueCountFrequency (%)
463
100.0%

읍면동명
Text

Distinct3277
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-04-06T17:29:12.176034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.5203432
Min length2

Characters and Unicode

Total characters12719
Distinct characters348
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3056 ?
Unique (%)84.6%

Sample

1st row청운효자동
2nd row사직동
3rd row삼청동
4th row부암동
5th row평창동
ValueCountFrequency (%)
중앙동 31
 
0.9%
남면 12
 
0.3%
서면 9
 
0.2%
북면 8
 
0.2%
송정동 7
 
0.2%
금성면 5
 
0.1%
신흥동 5
 
0.1%
동면 5
 
0.1%
교동 5
 
0.1%
성산면 4
 
0.1%
Other values (3267) 3522
97.5%
2024-04-06T17:29:13.246692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2293
 
18.0%
면 1212
 
9.5%
1 399
 
3.1%
2 388
 
3.1%
제 354
 
2.8%
산 298
 
2.3%
읍 258
 
2.0%
3 169
 
1.3%
신 159
 
1.3%
성 158
 
1.2%
Other values (338) 7031
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11568
91.0%
Decimal Number 1118
 
8.8%
Other Punctuation 33
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2293
 
19.8%
면 1212
 
10.5%
제 354
 
3.1%
산 298
 
2.6%
읍 258
 
2.2%
신 159
 
1.4%
성 158
 
1.4%
천 153
 
1.3%
장 148
 
1.3%
대 136
 
1.2%
Other values (327) 6399
55.3%
Decimal Number
ValueCountFrequency (%)
1 399
35.7%
2 388
34.7%
3 169
15.1%
4 80
 
7.2%
5 35
 
3.1%
6 22
 
2.0%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%
0 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11568
91.0%
Common 1151
 
9.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2293
 
19.8%
면 1212
 
10.5%
제 354
 
3.1%
산 298
 
2.6%
읍 258
 
2.2%
신 159
 
1.4%
성 158
 
1.4%
천 153
 
1.3%
장 148
 
1.3%
대 136
 
1.2%
Other values (327) 6399
55.3%
Common
ValueCountFrequency (%)
1 399
34.7%
2 388
33.7%
3 169
14.7%
4 80
 
7.0%
5 35
 
3.0%
. 33
 
2.9%
6 22
 
1.9%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11568
91.0%
ASCII 1151
 
9.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2293
 
19.8%
면 1212
 
10.5%
제 354
 
3.1%
산 298
 
2.6%
읍 258
 
2.2%
신 159
 
1.4%
성 158
 
1.4%
천 153
 
1.3%
장 148
 
1.3%
대 136
 
1.2%
Other values (327) 6399
55.3%
ASCII
ValueCountFrequency (%)
1 399
34.7%
2 388
33.7%
3 169
14.7%
4 80
 
7.0%
5 35
 
3.0%
. 33
 
2.9%
6 22
 
1.9%
7 11
 
1.0%
8 7
 
0.6%
9 5
 
0.4%

전체 평균연령
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.268503
Minimum30.8
Maximum68.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:29:13.698391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.8
5-th percentile39.4
Q144.2
median48.4
Q357.8
95-th percentile62.7
Maximum68.2
Range37.4
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation7.7160336
Coefficient of variation (CV)0.15349639
Kurtosis-1.0745869
Mean50.268503
Median Absolute Deviation (MAD)5.5
Skewness0.24809463
Sum181620.1
Variance59.537175
MonotonicityNot monotonic
2024-04-06T17:29:14.102200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.3 31
 
0.9%
47.2 30
 
0.8%
45.0 29
 
0.8%
45.5 29
 
0.8%
46.3 28
 
0.8%
46.0 28
 
0.8%
43.9 28
 
0.8%
44.7 28
 
0.8%
44.9 28
 
0.8%
44.6 27
 
0.7%
Other values (317) 3327
92.1%
ValueCountFrequency (%)
30.8 1
< 0.1%
32.6 1
< 0.1%
32.7 1
< 0.1%
33.1 1
< 0.1%
33.2 1
< 0.1%
33.3 1
< 0.1%
33.4 1
< 0.1%
33.8 1
< 0.1%
33.9 2
0.1%
34.0 1
< 0.1%
ValueCountFrequency (%)
68.2 1
 
< 0.1%
67.0 1
 
< 0.1%
66.7 1
 
< 0.1%
66.4 1
 
< 0.1%
65.9 1
 
< 0.1%
65.7 3
0.1%
65.6 2
 
0.1%
65.5 3
0.1%
65.3 2
 
0.1%
65.0 6
0.2%

남자 평균연령
Real number (ℝ)

HIGH CORRELATION 

Distinct301
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.591309
Minimum30.8
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:29:14.399978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.8
5-th percentile38.7
Q143.2
median46.9
Q355.3
95-th percentile59.8
Maximum65
Range34.2
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation6.9641279
Coefficient of variation (CV)0.14332044
Kurtosis-1.0496987
Mean48.591309
Median Absolute Deviation (MAD)5
Skewness0.24389712
Sum175560.4
Variance48.499077
MonotonicityNot monotonic
2024-04-06T17:29:14.683628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.3 35
 
1.0%
45.2 32
 
0.9%
46.0 31
 
0.9%
45.0 31
 
0.9%
45.4 31
 
0.9%
44.2 30
 
0.8%
43.7 30
 
0.8%
43.1 30
 
0.8%
45.3 30
 
0.8%
42.6 29
 
0.8%
Other values (291) 3304
91.4%
ValueCountFrequency (%)
30.8 1
 
< 0.1%
32.6 1
 
< 0.1%
32.8 2
0.1%
33.1 1
 
< 0.1%
33.2 1
 
< 0.1%
33.6 4
0.1%
33.7 1
 
< 0.1%
33.9 1
 
< 0.1%
34.0 1
 
< 0.1%
34.1 3
0.1%
ValueCountFrequency (%)
65.0 1
 
< 0.1%
64.3 1
 
< 0.1%
64.1 2
0.1%
63.2 1
 
< 0.1%
63.1 1
 
< 0.1%
63.0 1
 
< 0.1%
62.9 2
0.1%
62.8 1
 
< 0.1%
62.7 2
0.1%
62.6 3
0.1%

여자 평균연령
Real number (ℝ)

HIGH CORRELATION 

Distinct359
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.984362
Minimum30.8
Maximum71.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.9 KiB
2024-04-06T17:29:15.009574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.8
5-th percentile40.1
Q145.3
median49.9
Q360.3
95-th percentile65.8
Maximum71.9
Range41.1
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.5473239
Coefficient of variation (CV)0.16442106
Kurtosis-1.0833284
Mean51.984362
Median Absolute Deviation (MAD)6.2
Skewness0.25458982
Sum187819.5
Variance73.056746
MonotonicityNot monotonic
2024-04-06T17:29:15.291127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.1 30
 
0.8%
45.5 29
 
0.8%
46.8 28
 
0.8%
47.3 28
 
0.8%
47.2 27
 
0.7%
45.9 27
 
0.7%
43.9 27
 
0.7%
44.1 26
 
0.7%
45.6 26
 
0.7%
48.3 25
 
0.7%
Other values (349) 3340
92.4%
ValueCountFrequency (%)
30.8 1
< 0.1%
32.4 1
< 0.1%
32.9 1
< 0.1%
33.0 1
< 0.1%
33.2 1
< 0.1%
33.5 1
< 0.1%
33.6 1
< 0.1%
33.7 1
< 0.1%
33.8 1
< 0.1%
33.9 1
< 0.1%
ValueCountFrequency (%)
71.9 1
< 0.1%
70.5 1
< 0.1%
69.8 1
< 0.1%
69.7 1
< 0.1%
69.4 1
< 0.1%
69.3 1
< 0.1%
69.2 2
0.1%
69.1 2
0.1%
69.0 1
< 0.1%
68.7 1
< 0.1%

Interactions

2024-04-06T17:29:06.713466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:04.119236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:04.947515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:05.955220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:06.883174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:04.345446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:05.139494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:06.137270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:07.590908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:04.559191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:05.363480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:06.328023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:07.806425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:04.763064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:05.715855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:29:06.546435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:29:15.551775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드시도명전체 평균연령남자 평균연령여자 평균연령
행정기관코드1.0000.9920.4750.4660.481
시도명0.9921.0000.5320.5300.542
전체 평균연령0.4750.5321.0000.9930.995
남자 평균연령0.4660.5300.9931.0000.980
여자 평균연령0.4810.5420.9950.9801.000
2024-04-06T17:29:15.958001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관코드전체 평균연령남자 평균연령여자 평균연령시도명
행정기관코드1.0000.4580.4370.4760.972
전체 평균연령0.4581.0000.9960.9970.239
남자 평균연령0.4370.9961.0000.9860.238
여자 평균연령0.4760.9970.9861.0000.244
시도명0.9720.2390.2380.2441.000

Missing values

2024-04-06T17:29:08.044001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:29:08.309496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

행정기관코드기준연월시도명시군구명읍면동명전체 평균연령남자 평균연령여자 평균연령
011110515002024-03-31서울특별시종로구청운효자동45.343.846.4
111110530002024-03-31서울특별시종로구사직동46.546.446.5
211110540002024-03-31서울특별시종로구삼청동49.748.650.6
311110550002024-03-31서울특별시종로구부암동46.745.248.0
411110560002024-03-31서울특별시종로구평창동46.445.247.5
511110570002024-03-31서울특별시종로구무악동44.644.644.7
611110580002024-03-31서울특별시종로구교남동43.943.144.6
711110600002024-03-31서울특별시종로구가회동47.846.349.1
811110615002024-03-31서울특별시종로구종로1.2.3.4가동50.151.248.3
911110630002024-03-31서울특별시종로구종로5.6가동46.146.845.4
행정기관코드기준연월시도명시군구명읍면동명전체 평균연령남자 평균연령여자 평균연령
360352800330002024-03-31전북특별자치도부안군행안면58.055.360.8
360452800340002024-03-31전북특별자치도부안군계화면60.457.863.0
360552800350002024-03-31전북특별자치도부안군보안면63.159.866.6
360652800360002024-03-31전북특별자치도부안군변산면55.653.058.3
360752800370002024-03-31전북특별자치도부안군진서면59.256.262.3
360852800380002024-03-31전북특별자치도부안군백산면63.159.167.0
360952800390002024-03-31전북특별자치도부안군상서면60.957.764.0
361052800400002024-03-31전북특별자치도부안군하서면61.958.465.3
361152800410002024-03-31전북특별자치도부안군줄포면60.257.163.3
361252800420002024-03-31전북특별자치도부안군위도면60.958.364.1